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PubMed1993

Integrated clinical decision support using an object-oriented database management system.

Clinical decision support systems assist in diagnosis, alert to abnormal conditions, and manage patient data. Such systems tend to emphasize one of these features at the expense of the others and tend to be developed on an ad hoc basis. In this work, a technique is developed for creating an integrated clinical decision support system wholly within a general-purpose object-oriented database management system. The diagnostic and alerting requirements are accomplished with encapsulated methods and a novel use of the object-oriented database management class-hierarchy structure. The patient data management requirement is satisfied by the normal data management capabilities of the object-oriented DBMS.

Aphasia↗
PubMed2004

Automated detection of immunofluorescently labeled cytomegalovirus-infected cells in isolated peripheral blood leukocytes using decision tree analysis.

BACKGROUND: Cytomegalovirus (CMV) infection continues to be a major problem for immunocompromised patients. Detection of viral antigens in leukocytes (antigenemia assay) is widely used for the diagnosis of CMV infection and for guiding antiviral therapy. The antigenemia technique, contingent upon the manual microscopic analysis of rare cells, is a laborious task that is subject to human error. In this study, we combine automated microscopy with artificial intelligence for reliable detection of fluorescently labeled CMV-infected cells. METHODS: Cytospin preparations of peripheral blood leukocytes were immunofluorescently labeled for the CMV lower matrix phosphoprotein (pp65) and scanned in the Rare Event Imaging System (REIS), a fully automated image cytometer. The REIS detected potential positive objects and digitally recorded 49 measured cellular features for each identified case. The measurement data of these objects were analyzed by the See5 decision tree (DT) algorithm to ascertain whether they were true-positive detections. RESULTS: The DT was built from the measurement data of 2,047 true- and 2,028 false-positive detections, collected from 32 patient samples. By designating misclassifications of false-negatives three times more costly, the 10-fold cross-validation sensitivity, specificity, and misclassification error of the assay was 94.3%, 56.2%, and 25%, respectively. The method was also validated using an independent test set of 21 patient samples, in which similar results were obtained. CONCLUSIONS: To our knowledge, this study represents the first attempt to improve the accuracy of rare event image cytometry through the implementation of artificial intelligence methodology. Results suggest that cost-sensitive decision tree analysis of digitally measured cellular features vastly improves the performance of rare event image cytometry.

Artificial Intelligence↗
PubMed1997

Combining tabular, rule-based, and procedural knowledge in computer-based guidelines for childhood immunization.

IMM/Serve is a computer program which implements the clinical guidelines for childhood immunization. IMM/Serve accepts as input a child's immunization history. It then indicates which vaccinations are due and which vaccinations should be scheduled next. The clinical guidelines for immunization are quite complex and are modified quite frequently. As a result, it is important that IMM/Serve's knowledge be represented in a format that facilitates the maintenance of that knowledge as the field evolves over time. To achieve this goal, IMM/Serve uses four representations for different parts of its knowledge base: (1) Immunization forecasting parameters that specify the minimum ages and wait-intervals for each dose are stored in tabular form. (2) The clinical logic that determines which set of forecasting parameters applies for a particular patient in each vaccine series is represented using if-then rules. (3) The temporal logic that combines dates, ages, and intervals to calculate recommended dates, is expressed procedurally. (4) The screening logic that checks each previous dose for validity is performed using a decision table that combines minimum ages and wait intervals with a small amount of clinical logic. A knowledge maintenance tool, IMM/Def, has been developed to help maintain the rule-based logic. The paper describes the design of IMM/Serve and the rationale and role of the different forms of knowledge used.

Age Factors↗
PubMed2002

Adaptive user interface customization through browsing knowledge capitalization.

Hypermedia data browsing is a mean for improving information access. However, the overload and the heterogeneity of medical information, as well as the multitude of possible navigational paths, turn the consultation of data into a difficult task. We present in this paper a solution for the development of adaptive user interfaces in a hypermedia data browsing environment. It is based on the capitalization of the users knowledge in the decision-making process, expressed in terms of navigational paths and of data presentation modes that are customized to the user's preferences and practice. This capitalization offers the user a way to automatically store and reuse the experience accumulated in browsing through patient records. We illustrate our approach with the implementation of HEMA, a clinical workstation prototype that we have specialized for the cardiology domain.

Artificial Intelligence↗
PubMed1995

The use of misclassification costs to learn rule-based decision support models for cost-effective hospital admission strategies.

Cost-effective health care is at the forefront of today's important health-related issues. A research team at the University of Pittsburgh has been interested in lowering the cost of medical care by attempting to define a subset of patients with community-acquire pneumonia for whom outpatient therapy is appropriate and safe. Sensitivity and specificity requirements for this domain make it difficult to use rule-based learning algorithms with standard measures of performance based on accuracy. This paper describes the use of misclassification costs to assist a rule-based machine-learning program in deriving a decision-support aid for choosing outpatient therapy for patients with community-acquired pneumonia.

Algorithms↗
PubMed2005

Bayesian networks: computer-assisted diagnosis support in radiology.

Medical knowledge is growing at an explosive rate. While the availability of pertinent data has the potential to make the task of diagnosis more accurate, it is also increasingly overwhelming for physicians to assimilate. Using artificial intelligence techniques, a computer can process large amounts of data to help physicians manage the growing body of medical knowledge and thereby make better decisions. Computer-assisted diagnosis support is of particular interest to the diagnostic imaging community because radiologists must integrate huge amounts of data in order to diagnose disease. Bayesian networks, among the most promising artificial intelligence techniques available, enable computers to store knowledge and estimate the probability of outcomes based on probability theory. The article describes what a Bayesian network is and how it works using a system in mammography for illustration. A comparison of Bayesian networks with other types of artificial intelligence methods, specifically neural networks and case-based reasoning, clarifies the unique features and the potential of these systems to aid radiologists in the decisions they make every day.

Bayes Theorem↗
PubMed1995

Formalisation of safety reasoning in protocols and hazard regulations.

Written protocols are often employed to guide patient care. For treatment within a clinical trial, compliance with the trial protocol may be critical in ensuring efficacy and safety. Previous empirical work has established generic safety principles for reasoning about adverse events in clinical trials and their formalisation has been applied in a decision support system for managing treatment plans in oncology. The same generic knowledge can be reused to generate specific safety clauses when designing new treatment plans. Typically, clinicians devise trial protocols relatively infrequently and so software aids, especially those assisting with regulatory/safety conformance, will encourage more effective use of their time. A similar approach to the formalisation of safety knowledge in the control of hazardous industrial processes is discussed.

Artificial Intelligence↗
PubMed1999

Otoneurological expert system for vertigo.

We have developed an otoneurological expert system (ONE) to aid the diagnostics of vertigo, to assist teaching and to implement a database for research. The ONE database is set to harvest data on patient history, signs and test results necessary for diagnostic work with vertiginous patients. A method based on pattern recognition was used in the reasoning process. Questions about symptoms, signs and test results are weighted and scored for each disease and the most likely disease is recognized from defined disease profiles. Missing information and uncertainties are solved with a method resembling fuzzy logic. ONE was validated by comparing diagnoses assessed by physicians with those provided by the system. It proved to be a valid decision-maker by solving 65% of the cases correctly, while the physicians' mean was 69%. To improve ONE further, a follow-up should be implemented for the patients, since diagnosing sudden deafness and Meniere's disease during the first visit is often impossible. We aim to obtain new information on diseases involving vertigo by applying adaptive computer applications, such as genetic algorithms, to the reasoning process.

Algorithms↗
PubMed1997

Structuring clinical practice guidelines in a relational database model for decision support on the Internet.

The rapid proliferation of clinical practice guidelines (CPGs) has made computerization increasingly useful to clinicians. Computerization, however, requires transformation of the content and logic of each guideline into a computer-accessible form. In this project, we sought to use a relational database to construct a generalized guideline knowledge base for use with Internet-based decision support applications. We hypothesized that knowledge representation schemes could be developed to capture guideline content and logic within the constraints of a relational database model. In this paper we describe a database schema based on a relational model for computerizing CPGs using a hybrid of structured and procedural knowledge representation schemes. We developed and refined this model in the context of five diverse CPGs and found it accommodated all necessary representational requirements.

Algorithms↗
PubMed1988

Clinical reasoning in nursing.

This paper traces the development of the concept of nursing diagnosis and the various approaches being used to explain the cognitive processes used by practitioners in diagnosing patient problems. Three main types of explanation are compared; hypothesis generation/testing, decision analysis, and the information processing model. The recent development of the latter approach within the field of artificial intelligence is described and expert system research introduced. Finally, the potential benefits of the advent of expert systems into nursing are discussed.

Decision Support Techniques↗
PubMed1997

Application of autonomous neural network systems to medical pattern classification tasks.

This paper presents a study of the application of autonomously learning multiple neural network systems to medical pattern classification tasks. In our earlier work, a hybrid neural network architecture has been developed for on-line learning and probability estimation tasks. The network has been shown to be capable of asymptotically achieving the Bayes optimal classification rates, on-line, in a number of benchmark classification experiments. In the context of pattern classification, however, the concept of multiple classifier systems has been proposed to improve the performance of a single classifier. Thus, three decision combination algorithms have been implemented to produce a multiple neural network classifier system. Here the applicability of the system is assessed using patient records in two medical domains. The first task is the prognosis of patients admitted to coronary care units; whereas the second is the prediction of survival in trauma patients. The results are compared with those from logistic regression models, and implications of the system as a useful clinical diagnostic tool are discussed.

Artificial Intelligence↗
PubMed2005

Large margin nearest neighbor classifiers.

The nearest neighbor technique is a simple and appealing approach to addressing classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. The employment of a locally adaptive metric becomes crucial in order to keep class conditional probabilities close to uniform, thereby minimizing the bias of estimates. We propose a technique that computes a locally flexible metric by means of support vector machines (SVMs). The decision function constructed by SVMs is used to determine the most discriminant direction in a neighborhood around the query. Such a direction provides a local feature weighting scheme. We formally show that our method increases the margin in the weighted space where classification takes place. Moreover, our method has the important advantage of online computational efficiency over competing locally adaptive techniques for nearest neighbor classification. We demonstrate the efficacy of our method using both real and simulated data.

Algorithms↗
PubMed1997

Utility of a software assistant in critical care case review.

A computer program (the Audit Assistant) was developed to help physicians review the care of critically ill emergency department (ED) patients. The program is an example of a new class of decision aids that serves to remind physicians to consider possibilities, not an artificial intelligence program that actually attempts to simulate clinical reasoning. The goal of such programs is to enable physicians to reduce errors--in this case to enable reviewers to notice more of the errors in care in the cases they are reviewing. The objective of this study was to demonstrate on a small set of complex cases that the tested computer program enables the physician to perform a better quality review. The issue of what constitutes improved case review is addressed. In the first part of the study, reviewers reviewed two mock charts without using the Audit Assistant and then immediately reviewed the charts again with the assistance of the program. The reviews were compared. In the second part of the study, a second reviewer also compared the utility of the review of the first reviewer alone and the Audit Assistant output as an aid to review, using an additional mock chart. Six emergency physicians participated; each was a quality assurance director for the ED of one Cleveland area hospital. For the physicians reviewing without the Audit Assistant, 41% of critical actions were listed by three or four reviewers. For those using the Audit Assistant, 83% of critical actions were listed by three or four reviewers. All reviewers preferred the Audit Assistant-suggested list to the critical action list generated by a previous reviewer not using the Audit Assistant (P < .02). Use of the Audit Assistant improved the completeness and the consistency of physician review of mock charts of critically ill ED patients in a small series of cases. The critical actions added for review were important, as demonstrated by the preferential addition of critical actions chosen by other reviewers who were not using the computer program.

Critical Care↗
PubMed2000

Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy.

The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in JAVA, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a "gold standard" database which exhibit spectral and spatial profiles which are most similar to a given query image. The system suggests the most likely diagnosis based on majority logic of the retrieved cases. The system was used to discriminate among three lymphoproliferative disorders and healthy cells. The system provided the correct classification in more than 83% of the cases studied. System performance was evaluated using rigorous statistical assessment and by comparison with human observers.

Decision Support Techniques↗
PubMed1994

The use of medical logic modules at LDS hospital.

The development of medical knowledge bases for use in a clinical information system (HELP) has been an ongoing goal at LDS Hospital in Salt Lake City, Utah, for the past 25 years. In building our medical knowledge base we felt the need to implement a decision support syntax which could capture the logic of our experts in a way that was not only executable, but also easily read and shared by others. During these 25 years we defined several simple syntaxes to express this medical logic. Our current approach is to cooperate with international standards groups (ASTM) and use the Arden Syntax for medical logic modules. We are working with the 3M Corporation in the joint development of an Arden Compiler for HELP. We plan to use the Arden Syntax initially to support our alert/reminder system and computerized management protocols.

Artificial Intelligence↗
PubMed2004

Child Health Improvement through Computer Automation: the CHICA system.

Clinical guidelines are prevalent but frequently not used. Computer reminder systems can improve adherence to guidelines but have not been widely adopted. We present a computer-based decision support system that combines these elements: 1) pediatric preventive care guidelines encoded in Arden Syntax; 2) a dynamic, scannable paper user interface; and 3) a HL7-compliant interface to existing electronic medical record systems. The result is a system that both delivers "just in time" patient-relevant guidelines to physicians during the clinical encounter and accurately captures structured data from all who interact with the system. The system performs these tasks while remaining sensitive to the workflow constraints of a busy outpatient pediatric practice.

Algorithms↗
PubMed2001

Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.

Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.

Acceleration↗